Monetate Reviews

I personally have not used this feature within Monetate

I am giving this neutral rating simply because I never dealt with this option.

It really is a set-it-and-forget-it tool. Once you get your experiment set up (and you can test it in your production environment before you take it live), as long as you selected the Majority Fit or Individual Fit experience before creating it, it does everything else for you and you can go in and check the analytics (live analytics!) at any point. Anything that has reached significance is separated from the other data points so it is clear it has reached significance, and even those which have not yet reached significance have the ability to show which variation is winning for each data point. It's very easy to read and understand, both graphically and in rows of data.

It's great if you're happy for the exposure to work based on one metric. I believe this is the same restriction on most/all market software options.

I have not had any exposure to this yet, but I am aware of it. Once our testing runs, I suppose I will have a better grasp on it.

It's pretty straightforward and works easily.

IFEs are difficult because the unique identifier on the site needs to be consistent. Our site gives each user a different unique identifier until they sign in, making it nearly impossible to target based on their identifier. Monetate should advise on a best practice for identifiers.

We really like Majority Fit - this is the only machine learning capability we have used. Ideally, it would be great if this could be built in a way where we can understand how long an experience should run for. The advice on the Knowledge Hub does not suggest an end point for Majority Fit experiences. This means that other than diverting traffic to a positive experience, we have no definitive way of knowing how long they should be online. Also, we can't assess the results of a Majority Fit experience, only that it was a success or a failure.

We love the flexibility of majority fit capability. It has allowed us to test as frequently as we like and provide us with data we can use going forward with other testing ideas. I personally do not have experience using the individual fit capability so I can not speak effectively to that.

We haven't used Majority Fit machine learning capabilities yet.

We are still in entry stages with machine learning, and are seeing success. With Majority Fit, these experiences reduce error and require less output from the business team as the campaign has a goal to optimize to the better offering. It's less risk for us and gets us answers in a shorter period of time, which is great.

Majority fit works very well. We love not having to worry that the main result of testing will be to show the "loser" too many times. This lets us test as frequently as we want. We don't have access to the individual fit testing, so I can't review that except to say that I think that it should be included in the regular testing platform and not require an additional purchase.

We have not used these features enough to give a good opinion on them.

Majority Fit is a great way to test something and then have the winner become a higher percentage of the experiment. The only thing I would change with this is the reporting. I find it a little confusing to read the analytics and see how it has changed over time.

I'm not sure I have enough experience with this to respond, or I don't know the terminology being used. When we've done dynamic tests it's been nice that Monetate makes decisions on A/B test populations based on the real-time results so far.

Very impressive machine learning capabilities to study the incremental lift monetate provides for experiences running.

We have run a few tests using IFE. The reporting aspect makes IFE difficult to explain to the business the benefit of the experience. There is not a clear way to show learnings or ROI in a tangible way. The Majority Fit experiences we've run seemed to have deemed a winner early on and the test was later run as a split test that showed a different winner. This made us not put too much faith in the majority fit experiences.

I did not use this tool as much, so don't have a real opinion.

Majority fit tests were a super improvement for us! It allowed us to gain access to meaningful data more quickly.

We are exploring this capability for our website. So far it has been an easy integration and we've seen successful results.

Personally I think it's an amazing feature to have the algorithm picking up the best split and serving more traffic. That makes a difference and helps mitigating the possible losses of weak splits during the experience time. It's much better than just adjusting the experience to the winning scenario after the fact. Can't tell much about the individual fit experiences as we didn't use this feature (yet).

It is not clear how to use this, even after some light training.

Haven't used this faction enough to give a true review. But I like the idea of the action.